Nothnagel Kerstin, Aslam Mohammed Farid
Population Health Sciences, Canynge Hall, Bristol Medical School, University of Bristol, Bristol, UK
Imperial College London, London, England, UK.
BJGP Open. 2025 Jan 2;8(4). doi: 10.3399/BJGPO.2024.0057. Print 2024 Dec.
This study evaluates the feasibility of remote deep venous thrombosis (DVT) diagnosis via ultrasound sequences facilitated by ThinkSono Guidance, an artificial intelligence (AI) app for point-of-care ultrasound (POCUS).
To assess the effectiveness of AI-guided POCUS conducted by non-specialists in capturing valid ultrasound images for remote diagnosis of DVT.
DESIGN & SETTING: Over a 3.5-month period, patients with suspected DVT underwent AI-guided POCUS conducted by non-specialists using a handheld ultrasound probe connected to the app. These ultrasound sequences were uploaded to a cloud dashboard for remote specialist review. Additionally, participants received formal DVT scans.
Patients underwent AI-guided POCUS using handheld probes connected to the AI app, followed by formal DVT scans. Ultrasound sequences acquired during the AI-guided scan were uploaded to a cloud dashboard for remote specialist review, where image quality was assessed, and diagnoses were provided.
Among 91 predominantly older female participants, 18% of scans were incomplete. Of the rest, 91% had sufficient quality, with 64% categorised by remote clinicians as 'compressible' or 'incompressible'. Sensitivity and specificity for adequately imaged scans were 100% and 91%, respectively. Notably, 53% were low risk, potentially obviating formal scans.
ThinkSono Guidance effectively directed non-specialists, streamlining DVT diagnosis and treatment. It may reduce the need for formal scans, particularly with negative findings, and extend diagnostic capabilities to primary care. The study highlights AI-assisted POCUS potential in improving DVT assessment.
本研究评估了通过ThinkSono Guidance（一款用于床旁超声检查（POCUS）的人工智能（AI）应用程序）辅助的超声序列进行远程深静脉血栓形成（DVT）诊断的可行性。
评估由非专科医生进行的人工智能引导的床旁超声检查在获取有效超声图像以进行DVT远程诊断方面的有效性。
在3.5个月的时间里，疑似DVT的患者由非专科医生使用连接到该应用程序的手持式超声探头进行人工智能引导的床旁超声检查。这些超声序列被上传到云平台以供远程专科医生审查。此外，参与者还接受了正式的DVT扫描。
患者使用连接到人工智能应用程序的手持式探头进行人工智能引导的床旁超声检查，随后进行正式的DVT扫描。在人工智能引导扫描过程中获取的超声序列被上传到云平台以供远程专科医生审查，在那里评估图像质量并给出诊断结果。
在91名主要为老年女性的参与者中，18%的扫描不完整。其余扫描中，91%质量足够，其中64%被远程临床医生分类为“可压缩”或“不可压缩”。对成像充分的扫描的敏感性和特异性分别为100%和91%。值得注意的是，53%为低风险，可能无需进行正式扫描。
ThinkSono Guidance有效地指导了非专科医生，简化了DVT的诊断和治疗。它可能减少对正式扫描的需求，特别是对于阴性结果，并将诊断能力扩展到初级保健。该研究突出了人工智能辅助床旁超声检查在改善DVT评估方面的潜力。
